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Author

*The author of this computation has been verified*

R Software Module

rwasp_smp.wasp

Title produced by software

Standard Deviation-Mean Plot

Date of computation

Sun, 18 Dec 2016 18:18:36 +0100

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/18/t14820816175w953h0f3tdg64m.htm/, Retrieved Wed, 08 May 2024 21:06:05 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=301193, Retrieved Wed, 08 May 2024 21:06:05 +0000

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Estimated Impact

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Standard Deviation-Mean Plot] [N2044 SD mean plot] [2016-12-18 17:18:36] [2e11ca31a00cf8de75c33c1af2d59434] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	0 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time0 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301193&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

0 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=301193&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301193&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	0 seconds
R Server	Big Analytics Cloud Computing Center

Standard Deviation-Mean Plot
Section	Mean	Standard Deviation	Range
1	3979.16666666667	131.041862383628	450
2	4105	124.133359372454	430
3	4110.83333333333	125.441644044576	400
4	4241.66666666667	168.352084164183	590
5	4411.66666666667	117.22808640605	430
6	4564.16666666667	124.71482621881	370
7	4699.16666666667	176.504356543367	660
8	4836.66666666667	155.874852676658	550
9	4724.16666666667	168.601646348768	650

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 3979.16666666667 & 131.041862383628 & 450 \tabularnewline
2 & 4105 & 124.133359372454 & 430 \tabularnewline
3 & 4110.83333333333 & 125.441644044576 & 400 \tabularnewline
4 & 4241.66666666667 & 168.352084164183 & 590 \tabularnewline
5 & 4411.66666666667 & 117.22808640605 & 430 \tabularnewline
6 & 4564.16666666667 & 124.71482621881 & 370 \tabularnewline
7 & 4699.16666666667 & 176.504356543367 & 660 \tabularnewline
8 & 4836.66666666667 & 155.874852676658 & 550 \tabularnewline
9 & 4724.16666666667 & 168.601646348768 & 650 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301193&T=1

[TABLE]
[ROW][C]Standard Deviation-Mean Plot[/C][/ROW]
[ROW][C]Section[/C][C]Mean[/C][C]Standard Deviation[/C][C]Range[/C][/ROW]
[ROW][C]1[/C][C]3979.16666666667[/C][C]131.041862383628[/C][C]450[/C][/ROW]
[ROW][C]2[/C][C]4105[/C][C]124.133359372454[/C][C]430[/C][/ROW]
[ROW][C]3[/C][C]4110.83333333333[/C][C]125.441644044576[/C][C]400[/C][/ROW]
[ROW][C]4[/C][C]4241.66666666667[/C][C]168.352084164183[/C][C]590[/C][/ROW]
[ROW][C]5[/C][C]4411.66666666667[/C][C]117.22808640605[/C][C]430[/C][/ROW]
[ROW][C]6[/C][C]4564.16666666667[/C][C]124.71482621881[/C][C]370[/C][/ROW]
[ROW][C]7[/C][C]4699.16666666667[/C][C]176.504356543367[/C][C]660[/C][/ROW]
[ROW][C]8[/C][C]4836.66666666667[/C][C]155.874852676658[/C][C]550[/C][/ROW]
[ROW][C]9[/C][C]4724.16666666667[/C][C]168.601646348768[/C][C]650[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301193&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301193&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Standard Deviation-Mean Plot
Section	Mean	Standard Deviation	Range
1	3979.16666666667	131.041862383628	450
2	4105	124.133359372454	430
3	4110.83333333333	125.441644044576	400
4	4241.66666666667	168.352084164183	590
5	4411.66666666667	117.22808640605	430
6	4564.16666666667	124.71482621881	370
7	4699.16666666667	176.504356543367	660
8	4836.66666666667	155.874852676658	550
9	4724.16666666667	168.601646348768	650

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	-37.7275147560269
beta	0.0411227008875855
S.D.	0.0236153036656766
T-STAT	1.7413581239421
p-value	0.125160752100466

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -37.7275147560269 \tabularnewline
beta & 0.0411227008875855 \tabularnewline
S.D. & 0.0236153036656766 \tabularnewline
T-STAT & 1.7413581239421 \tabularnewline
p-value & 0.125160752100466 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301193&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-37.7275147560269[/C][/ROW]
[ROW][C]beta[/C][C]0.0411227008875855[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0236153036656766[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.7413581239421[/C][/ROW]
[ROW][C]p-value[/C][C]0.125160752100466[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301193&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301193&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	-37.7275147560269
beta	0.0411227008875855
S.D.	0.0236153036656766
T-STAT	1.7413581239421
p-value	0.125160752100466

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	-5.31536769033902
beta	1.22427312395488
S.D.	0.719871227170896
T-STAT	1.70068350803003
p-value	0.13279688046882
Lambda	-0.224273123954884

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -5.31536769033902 \tabularnewline
beta & 1.22427312395488 \tabularnewline
S.D. & 0.719871227170896 \tabularnewline
T-STAT & 1.70068350803003 \tabularnewline
p-value & 0.13279688046882 \tabularnewline
Lambda & -0.224273123954884 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301193&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-5.31536769033902[/C][/ROW]
[ROW][C]beta[/C][C]1.22427312395488[/C][/ROW]
[ROW][C]S.D.[/C][C]0.719871227170896[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.70068350803003[/C][/ROW]
[ROW][C]p-value[/C][C]0.13279688046882[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.224273123954884[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301193&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301193&T=3

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	-5.31536769033902
beta	1.22427312395488
S.D.	0.719871227170896
T-STAT	1.70068350803003
p-value	0.13279688046882
Lambda	-0.224273123954884

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 6 ; par2 = Double ; par3 = additive ; par4 = 12 ;

Parameters (R input):

par1 = 12 ;

R code (references can be found in the software module):

par1 <- as.numeric(par1)
(n <- length(x))
(np <- floor(n / par1))
arr <- array(NA,dim=c(par1,np))
j <- 0
k <- 1
for (i in 1:(np*par1))
{
j = j + 1
arr[j,k] <- x[i]
if (j == par1) {
j = 0
k=k+1
}
}
arr
arr.mean <- array(NA,dim=np)
arr.sd <- array(NA,dim=np)
arr.range <- array(NA,dim=np)
for (j in 1:np)
{
arr.mean[j] <- mean(arr[,j],na.rm=TRUE)
arr.sd[j] <- sd(arr[,j],na.rm=TRUE)
arr.range[j] <- max(arr[,j],na.rm=TRUE) - min(arr[,j],na.rm=TRUE)
}
arr.mean
arr.sd
arr.range
(lm1 <- lm(arr.sd~arr.mean))
(lnlm1 <- lm(log(arr.sd)~log(arr.mean)))
(lm2 <- lm(arr.range~arr.mean))
bitmap(file='test1.png')
plot(arr.mean,arr.sd,main='Standard Deviation-Mean Plot',xlab='mean',ylab='standard deviation')
dev.off()
bitmap(file='test2.png')
plot(arr.mean,arr.range,main='Range-Mean Plot',xlab='mean',ylab='range')
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Standard Deviation-Mean Plot',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Section',header=TRUE)
a<-table.element(a,'Mean',header=TRUE)
a<-table.element(a,'Standard Deviation',header=TRUE)
a<-table.element(a,'Range',header=TRUE)
a<-table.row.end(a)
for (j in 1:np) {
a<-table.row.start(a)
a<-table.element(a,j,header=TRUE)
a<-table.element(a,arr.mean[j])
a<-table.element(a,arr.sd[j] )
a<-table.element(a,arr.range[j] )
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Regression: S.E.(k) = alpha + beta * Mean(k)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,lm1$coefficients[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'beta',header=TRUE)
a<-table.element(a,lm1$coefficients[[2]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,summary(lm1)$coefficients[2,2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,summary(lm1)$coefficients[2,3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,summary(lm1)$coefficients[2,4])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,lnlm1$coefficients[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'beta',header=TRUE)
a<-table.element(a,lnlm1$coefficients[[2]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,summary(lnlm1)$coefficients[2,2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,summary(lnlm1)$coefficients[2,3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,summary(lnlm1)$coefficients[2,4])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Lambda',header=TRUE)
a<-table.element(a,1-lnlm1$coefficients[[2]])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable2.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code